Load metadata: Metadata strains with plate layout (indicating which strain is in which well) and database with taxonomic information about the strains.
Load growth data: Growth data from the plates.
Formatting:Data from the different runs are merged, then the time intervals between the measurements are calculated and last then the results are joined with the strain metadata of the strains. Next the density increase is calculated by subtracting the first measurement (represents inoculation density) from all subsequent measurements. This result data file is saved as raw results.
First the density increase in the medium control plate is plotted to check that there was no general contamination detected in the experiment.
Next the growth of all strains in the control treatment and the no bacteria control is checked.
Here the growth of two bacterial strains in one run in 6 concnetrations of MBOA and the control treatment are plotted. Pseudomonas LPD2 is MBOA-tolerant whil LRH8.O is MBOA susceptible.
Remove strains from the analysis: For some strains bad growth was detected in the growth curve which are removed due to bad growth, possible contamination, unclear taxonomy and antibiotic tolerant strains.
To quantify the total bacterial growth over time, we calculate the total area under the curve. This is done with the function “auc()” For comparison between treatments, the total AUC (AUC_raw) is normalized with the AUC of the strain grown in the control treatment (no chemicals added, just normal growth media with DMSO). The AUC_norm is used for all further analysis, plotting and calculations.
Figures for supplement
All AUC MBOA
AUC tolerant and susceptible strain in MBOA
All AUC BOA
All AUC AMPO
All AUC APO
All AUC DIMBOA-Glc
MBOA and AMPO at 50 μM: Compare low MBOA concentrations with high AMPO concentrations [50 uM]
To investigate how many strains are susceptible to low concentrations of MBOA and AMPO (50 μM). We check for the number of strains with AUC_norm < 0.8 and statistically test (t.test) which bacteria grow significantly less in the treatment compared to the control.
| Strain |
|---|
| LWO6 |
| Strain |
|---|
| LST15 |
| LWO6 |
| Strain |
|---|
| LAC11 |
| LBA112 |
| LBA20 |
| LMB2 |
| LMD1 |
| LME1 |
| LME3 |
| LMI1 |
| LMI11 |
| LMI13 |
| LMJ1 |
| LML1 |
| LMO1 |
| LMS1 |
| LMX3 |
| LMX9231 |
| LMY1 |
| LMZ1 |
| LPA2 |
| LPB4.O |
| LPE13 |
| LRH11 |
| LSP13 |
| LWO6 |
| Strain |
|---|
| LAR21 |
| LBA1 |
| LBA112 |
| LBA20 |
| LBA21 |
| LBA3 |
| LBA71 |
| LMA1 |
| LMB2 |
| LMC3 |
| LMD1 |
| LME1 |
| LME2 |
| LMI1 |
| LMI11 |
| LMI13 |
| LMI15 |
| LMI1x |
| LMJ1 |
| LML1 |
| LMN1 |
| LMO1 |
| LMS1 |
| LMX3 |
| LMX7 |
| LPA2 |
| LRH11 |
| LRH8.O |
| LSP13 |
| LST14 |
| LWO6 |
To investigate how many strains are susceptible to high concentrations of MBOA and BOA (2500 μM). We check for the number of strains with AUC_norm < 0.75 and statistically test (t.test) which bacteria grow significantly less in the treatment compared to the control.
| Strain |
|---|
| LBA1 |
| LBA3 |
| LBA71 |
| LMB2 |
| LME1 |
| LME2 |
| LMI11 |
| LMJ1 |
| LMX9 |
| LMY1 |
| LPB4.O |
| LPD11 |
| LPD2 |
| LPE13 |
To compare the tolerance among different bacterial strains, we use the tolerance index (TI). This tolerance index is calculated from the area under the curve of the AUC of each strain in the different concentrations of the compounds, as further normalization the normalized tolerance index is calculated from AUC_norm instead of AUC. Accordingly, a strain with TI_norm = 1 is completely tolerant to the compound in each concentration (not inhibited by the compound). Tolerance index is calculated for each compound separately using results_AUC from the respective compound.
TI MBOA
Tolerance index of tolerant and susceptible strain in MBOA
TI MBOA without high concentrations
TI MBOA at two concentrations (500 and 2500 μM)
AUC MBOA 625 μM
TI BOA
TI AMPO
AUC DIMBOA-Glc 2500 uM
TI DIMBOA-Glc
correlation TI with TI low
TI APO
p-value APO
To investigate if the tolerance of maize root bacteria to different benzoxazinonids and aminophenoxazinones depend on each other, we correlated the TIs of all bacteria in the different chemicals with each other.
MBOA ~ AMPO
MBOA ~ BOA
APO ~ AMPO
BOA ~ APO
MBOA ~ DIMBOA-Glc
| MBOA_type | MBOA | BOA_type | BOA | AMPO_type | AMPO | APO_type | APO |
|---|---|---|---|---|---|---|---|
| intermediate | 18 | intermediate | 30 | intermediate | 4 | intermediate | 7 |
| susceptible | 22 | susceptible | 6 | susceptible | 4 | susceptible | 9 |
| tolerant | 12 | tolerant | 16 | tolerant | 44 | tolerant | 36 |
| Compound | p.value |
|---|---|
| MBOA | 0.00010910830639201 |
| BOA | 2.5851643475067e-05 |
| AMPO | 0.0559324299277804 |
| APO | 0.00723728836215448 |
Load metadata: Metadata strains with plate layout (indicating which strain is in which well) and database with taxonomic information about the strains.
Load growth data: Growth data from the plates.
Calculate density increase: substract initial density from density of each time point
First the density increase in the medium control plate is plotted to check that there was no general contamination detected in the experiment.
Next the growth of all strains in the control treatment and the no bacteria control is checked.
Growth of individual strains in different concentrations of MBOA.
Some strains did not grew and were therefore excluded from the analysis. In both runs: Root1294 Sphigomonas, Root420 Flavobacterium, Root482 Rhizobium, Root559 Lysobacter, Root630 Pseudoxanthomonas, in run 1: Root318D1 Variovorax, in run 2: Root166 Microbacterium.
To quantify the total bacterial growth over time, we calculate the total area under the curve. This is done with the function “auc()” For comparison between treatments, the total AUC (AUC_raw) is normalized with the AUC of the strain grown in the control treatment (no chemicals added, just normal growth media with DMSO). The AUC_norm is used for all further analysis, plotting and calculations.
To compare the tolerance among different bacterial strains, we use the tolerance index. The tolerance index is calculated from the area under the curve of the AUC of each strain in the different concentrations of the compounds, as further normalization the tolerance index is calculated from AUC_norm instead of AUC. Accordingly, a strain with TI_norm = 1 is completely tolerant to the compound in each concentration (not inhibited by the compound). Tolerance index is calculated for each compound separately using results_AUC from the respective compound.
fisher exact
| Compound | p.value |
|---|---|
| MBOA | 0.237658243023882 |
We mapped the 16s rRNA sequences to the microbiome dataset published by Hu et al. 2018. In this study, maize wild-type plants (producing BXs) were grown along with bx1 mutant plants in the field. Then the plants were harvested and the root, the rhizosphere and the soil compartment were sequenced. The bacterial community profiles of wild-type and bx1 mutant plants differed significantely. We found that most of the MRB isolates map to taxonomic units (OTUs) in the dataset and many of them map to abundant OTUs. Here we investigate the BX-dependent colonization of these OTUs in the microbiome dataset and correlate those with the tolerance index of the strains.
In this graph the log2foldchange of OTUs on WT/bx1 roots in the field is visualized.
Stats was done like this: iso_abundance_single_data_long %>% group_by(OTU, compartment) %>% do(tidy(t.test(Abundance~genotype, data=.))) %>% add_significance(“p.value”) %>% dplyr::select(OTU, p.value, p.value.signif)
MBOA root
MBOA rhizosphere
BOA
AMPO
APO
DIMBOA-Glc
DIMBOA-Glc
sessionInfo()
## R version 4.3.1 (2023-06-16 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 11 x64 (build 22621)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=English_Switzerland.utf8 LC_CTYPE=English_Switzerland.utf8
## [3] LC_MONETARY=English_Switzerland.utf8 LC_NUMERIC=C
## [5] LC_TIME=English_Switzerland.utf8
##
## time zone: Europe/Berlin
## tzcode source: internal
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] gtable_0.3.4 ggbeeswarm_0.7.2 ggpmisc_0.5.4-1 ggpp_0.5.4
## [5] ggpubr_0.6.0 ggforce_0.4.1 ggplot2_3.4.3 ggthemes_4.2.4
## [9] rstatix_0.7.2 purrr_1.0.2 readr_2.1.4 stringr_1.5.0
## [13] tidyr_1.3.0 dplyr_1.1.3 broom_1.0.5 lubridate_1.9.2
## [17] readxl_1.4.3 magrittr_2.0.3 emmeans_1.8.8 multcomp_1.4-25
## [21] TH.data_1.1-2 MASS_7.3-60 survival_3.5-5 mvtnorm_1.2-3
## [25] lmerTest_3.1-3 lme4_1.1-34 Matrix_1.6-1 metacoder_0.3.6
## [29] MESS_0.5.12
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.0 vipor_0.4.5 farver_2.1.1
## [4] fastmap_1.1.1 tweenr_2.0.2 labelled_2.12.0
## [7] digest_0.6.33 estimability_1.4.1 timechange_0.2.0
## [10] lifecycle_1.0.3 compiler_4.3.1 rlang_1.1.1
## [13] sass_0.4.7 tools_4.3.1 utf8_1.2.3
## [16] yaml_2.3.7 geeM_0.10.1 knitr_1.43
## [19] ggsignif_0.6.4 labeling_0.4.3 ggformula_0.10.4
## [22] ggstance_0.3.6 plyr_1.8.8 abind_1.4-5
## [25] withr_2.5.0 numDeriv_2016.8-1.1 geepack_1.3.9
## [28] polyclip_1.10-4 mosaicCore_0.9.2.1 fansi_1.0.4
## [31] colorspace_2.1-0 scales_1.2.1 ggridges_0.5.4
## [34] cli_3.6.1 crayon_1.5.2 rmarkdown_2.24
## [37] generics_0.1.3 rstudioapi_0.15.0 tzdb_0.4.0
## [40] polynom_1.4-1 minqa_1.2.5 cachem_1.0.8
## [43] splines_4.3.1 cellranger_1.1.0 vctrs_0.6.3
## [46] boot_1.3-28.1 sandwich_3.0-2 SparseM_1.81
## [49] jsonlite_1.8.7 carData_3.0-5 car_3.1-2
## [52] hms_1.1.3 beeswarm_0.4.0 clipr_0.8.0
## [55] jquerylib_0.1.4 rematch_2.0.0 glue_1.6.2
## [58] nloptr_2.0.3 codetools_0.2-19 stringi_1.7.12
## [61] munsell_0.5.0 tibble_3.2.1 pillar_1.9.0
## [64] quantreg_5.97 htmltools_0.5.6 R6_2.5.1
## [67] evaluate_0.21 lattice_0.21-8 highr_0.10
## [70] haven_2.5.3 backports_1.4.1 bslib_0.5.1
## [73] MatrixModels_0.5-2 Rcpp_1.0.11 nlme_3.1-162
## [76] mgcv_1.8-42 xfun_0.40 zoo_1.8-12
## [79] forcats_1.0.0 pkgconfig_2.0.3